The convolution neural network based agent vehicle detection using forward-looking sonar image

2016 ◽  
Author(s):  
Juhwan Kim ◽  
Hyeonwoo Cho ◽  
Juhyun Pyo ◽  
Byeongjin Kim ◽  
Son-Cheol Yu
Keyword(s):  
Neural Network ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Sonar Image ◽  
Forward Looking

scholarly journals A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250782
Author(s):  
Bin Wang ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Semantic Features ◽  
General Object ◽  
High Level ◽  
The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

scholarly journals Automated Failure Detection in Computer Vision Systems

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
H. Yan ◽  
A. Achkar ◽  
Akshaya Mishra ◽  
K. Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vehicle Detection ◽  
Failure Detection ◽  
Convolution Neural Network ◽  
Vision Systems ◽  
Testing Data ◽  
Computer Vision Systems ◽  
Human Validation

Human validation of computer vision systems increase their operatingcosts and limits their scale. Automated failure detection canmitigate these constraints and is thus of great importance to thecomputer vision industry. Here, we apply a deep neural networkto detect computer vision failures on vehicle detection tasks. Theproposed model is a convolution neural network that estimates theoutput quality of a vehicle detector. We train the network to learnto estimate a pixel-level F1 score between the vehicle detector andhuman annotated data. The model generalizes well to testing data,providing a mechanism for identifying detection failures.

scholarly journals Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

2021 ◽  
Author(s):  
Yuguang Ye
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Sparse Representation ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Response Speed ◽  
Gaussian Mixture ◽  
Image Features ◽  
Convolution Neural Network ◽  
Processing Efficiency

Abstract With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

scholarly journals Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4588 ◽  
Author(s):  
Ahmed Gomaa ◽  
Moataz M. Abdelwahab ◽  
Mohammed Abo-Zahhad ◽  
Tsubasa Minematsu ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Fixed Number ◽  
Convolution Neural Network ◽  
Feature Points ◽  
Motion Feature ◽  
Moving Vehicles ◽  
Detection And Counting

Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle’s robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle’s trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches.

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